abstract = "Software modelling activities typically involve a
tedious and time-consuming effort by specially trained
personnel. This lack of automation hampers the adoption
of the Model Driven Engineering (MDE) paradigm.
Nevertheless, in the recent years, much research work
has been dedicated to learn MDE artefacts instead of
writing them manually. In this context, mono- and
multi-objective Genetic Programming (GP) has proven
being an efficient and reliable method to derive
automation knowledge by using, as training data, a set
of input/out examples representing the expected
behaviour of an artefact. Generally, the conformance to
the training example set is the main objective to lead
the search for a solution. Yet, single fitness peak, or
local optima deadlock, one of the major drawbacks of
GP, happens when adapted to MDE and hinder the results
of the learning. We aim at showing in this paper that
an improvement in population social diversity carried
out during the evolutionary",